System PHM Algorithm Maturation
Résumé
a Safran Snecma, rond point René Ravaud 77550, Moissy Cramayel, France b Safran Engineering Services, rond point René Ravaud 77550, The maturation of PHM functions is focused on two Key Performance Indicators (KPI): The NFF, No Fault Found ratio, i.e. probability of a fault detection to be unverified, and the Probability Of Detection POD The estimation of the second KPI can be done by counting the global abnormality threshold trespassing when each different kind of degradation is simulated. The first KPI is rather a requirement. It induces a constraint on the threshold position in terms of probability of threshold trespassing with no degradation. Typically, for a probability of fault occurrence of 10-7 , a specified NFF ratio of 1 %, and an expected POD of 90 %, the order of magnitude of probability of threshold trespassing with no degradation should be 10-9. The estimation of such extreme level of probability needs some parametric adjustment of the distribution of the global abnormality score with no degradation. Two PHM functions are considered as case studies: Turbofan engine start capability (ESC) and turbofan engine lubrication oil consumption (EOC). In ESC the global abnormality score is a norm of a vector of specific abnormality scores. Some specific scores are devoted to starter air supply. In EOC, the global abnormality score is the consumption estimation. To reach acceptable POD at the specified NFF ratio three improvements are needed for ESC:-Adjust the abnormality decision threshold according to each candidate degradation using extreme value quantiles on the global abnormality score distribution-Average the global abnormality score on five consecutive starts-Learn the regression relations specifically on each engine. The first improvement is a novelty. It is successfully applied to both ESC and EOC functions. It is generic to all airborne system PHM functions based on abnormality scores.